Landscape-scale evaluation of genetic structure among barrier-isolated populations of coastal cutthroat trout,<i>Oncorhynchus clarkii clarkii</i>
Bibliographic record
Abstract
Relationships among landscape structure, stochastic disturbance, and genetic diversity were assessed by examining interactions between watershed-scale environmental factors and genetic diversity of coastal cutthroat trout ( Oncorhynchus clarkii clarkii ) in 27 barrier-isolated watersheds from western Oregon, USA. Headwater populations of coastal cutthroat trout were genetically differentiated (mean FST= 0.33) using data from seven microsatellite loci (2232 individuals), but intrapopulation microsatellite genetic diversity (mean number of alleles per locus = 5, mean He= 0.60) was only moderate. Genetic diversity of coastal cutthroat trout was greater (P = 0.02) in the Coast Range ecoregion (mean alleles = 47) than in the Cascades ecoregion (mean alleles = 30), and differences coincided with indices of regional within-watershed complexity and connectivity. Furthermore, regional patterns of diversity evident from isolation-by-distance plots suggested that retention of within-population genetic diversity in the Coast Range ecoregion is higher than that in the Cascades, where genetic drift is the dominant factor influencing genetic patterns. Thus, it appears that physical landscape features have influenced genetic patterns in these populations isolated from short-term immigration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".